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Cell Fate Relationships Mapped by p-Creode Trajectory Analysis of Single-cell Data

dc.creatorHerring, Charles Albert
dc.description.abstractModern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, introduced is p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. p-Creode is applied to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell-state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories.
dc.subjectdifferentiation hierarchies
dc.subjectpseudo-time analysis
dc.subjectsingle-cell RNA-seq
dc.subjectintestine and colon
dc.subjectmass cytometry
dc.subjectgraph theory
dc.subjecttuft cells
dc.subjectcell-state transitions
dc.subjectsingle-cell biology
dc.titleCell Fate Relationships Mapped by p-Creode Trajectory Analysis of Single-cell Data
dc.contributor.committeeMemberJohn Anthony Capra
dc.contributor.committeeMemberErin Rericha
dc.contributor.committeeMemberKen Lau
dc.contributor.committeeMemberGregor Neuert
dc.type.materialtext and Physical Biology University
dc.contributor.committeeChairVito Quaranta

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